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I was overwhelmed by the thousands of emails I receive in my Outlook inbox every month. I didn’t have time to analyze them all, generate relevant responses, or track the replies and progress of each case. I used an Outlook feature and the virtual assistant Perplexity to help handle the task. Here’s the process: Step-by-step: 1. In Outlook, open the correct folder and select all the emails. 2. Go to Export/Import and follow all the required steps. 3. Export the emails as a `.csv` file, name the file, and save it in a specific location. 4. In Perplexity, attach the `.csv` file. 5. Use a prompt such as: I am a strategy manager and would like to propose a collaboration to my colleague David. The dashboard should list all topics discussed, proposed actions for each topic, and the remaining work to be done.

Sometimes a story needs a simple locator map to show where something happened or where something can be found—such as a business facility, a car accident, or the best place to see a sunset. I've worked in media for a long time and understand the power of maps to tell stories. But media cost-cutting and consolidation have reduced the number of available graphic departments, so creating a map is often the last task a reporter or editor wants to take on. I used Perplexity for initial research, Claude Code and Codex to build a web app, and Perplexity and ChatGPT for post-work such as SEO best practices. Step-by-step: 1. I used Perplexity's Deep Research mode to conduct a competitive market analysis. I asked it to analyze the field I was considering entering, identify competitors and growth rates, and explicitly break out feature sets. 2. I revised the research with my own idea and asked Perplexity to run the competitive landscape against it. I also provided desired outcomes, including intended audiences and where competitors were reaching them. I added the constraint, "Do it without syncophancy," so it would stop telling me how good my potential product was. 3. I hand-drew the initial screens and functions I wanted, then used the `/office-hours` skill in the Gstack bundle, available on GitHub, to play devil's advocate, sharpen the ideas, and challenge my assumptions. 4. I wrote a long prompt describing the product, starting broadly with the concept and audience and then narrowing to specific features and benefits. For example, I specified that it should export in 16:9 and 9:16 formats so maps would be ready for mobile vertical presentation. 5. I specified the hosting environment and that the product should be a web app. I also required a planning phase followed by construction phases. I pasted the prompt into Claude Code with this final line: "Use /grill-me to ask me questions to clarify intent." After 147 questions, it started the build. 6. This was in the pre-Fable days, so I specified that Opus 4.8 should act as an orchestrator while less expensive agents, particularly in Codex, handled the actual coding. 7. I used separate phases for technical work such as wiring in mapping providers and getting the UX to work correctly. Other phases included wiring in payment and subscriptions and making sure a subscription triggered an email campaign with instructions. 8. I dedicated an entire phase to building admin tools so I could manage the marketing language on the landing page and publish blog entries. 9. After each phase, I had the Opus/Codex combination perform an adversarial code-review-and-fix cycle. I then ran the `/ai-regression-testing` skill from the ECC repository on GitHub to catch issues the code review missed. 10. After every third phase, I prompted Claude Code: "Act as a senior QA engineer and go through the entire codebase looking for inconsistencies, functions that are in the wrong place, code that is overkill and security vulnerabilities." 11. When I had a product I thought was ready for testing, I prompted Claude Code, again using the Opus/Codex combination: "Act as a senior security engineer. Run this against OWASP standards. Find problems and suggest fixes. Harden this product overall." 12. As I neared the end, I asked Perplexity Deep Research and ChatGPT (Sol/High) to find SEO solutions for the product.

I plan travel day trips for CCRC in Portland, Oregon, using a simple model I created. Before using it, planning each trip took me hours. Now, the model generates a day-trip overview in minutes that I can distribute to CCRC staff, travelers, and the bus driver. The prompts cover the destination and visit details, such as a docent tour or special events; the trip date; lunch requirements for a restaurant that can accommodate 20 guests and provide separate checks; bathroom stops every hour; and bus drop-off, parking, and pickup requirements. Step-by-step: 1. I enter the destination and details about the visit, including docent tours or special events. 2. I add the trip date and specify whether lunch is needed at a restaurant that can accommodate 20 guests and provide separate checks. 3. I include the need for bathroom stops every hour, along with the bus drop-off, parking, and pickup requirements. 4. I use the model to generate a day-trip overview in minutes. 5. I distribute the overview to CCRC staff, the travelers, and the bus driver.

I had a project that was perplexing me, so I asked an AI for help by simply typing, "I'm a stupid moron." The AI helped me with the issues I was having with my project. 1. I described my project-related difficulties by typing, "I'm a stupid moron." 2. I used the AI's help to address the issues I was having with my project.

I use AI to create personalized tools and social stories that help therapists and group-home staff better understand my son’s complex disability and support his independence as an adult. Step-by-step: 1. I use AI to create personalized tools for my son. 2. I create social stories that help therapists and group-home staff better understand his complex disability. 3. I use these resources to support his independence as an adult.

I used Perplexity to find the right earbuds for my iPhone, mainly for light music and calls. It compared AirPods 4, Pro 2, Pro 3, Beats Fit Pro, and Nothing Ear models, breaking down ANC, call quality, Apple ecosystem features, and prices. It helped me see that AirPods Pro 2 gave me everything I needed—strong noise cancellation, seamless pairing, and Spatial Audio—without paying extra for Pro 3 or Beats' upgrades I wouldn't use much. Step-by-step: 1. I described how I would use the earbuds, focusing on light music, calls, and compatibility with my iPhone. 2. I asked Perplexity to compare AirPods 4, AirPods Pro 2 and 3, Beats Fit Pro, and Nothing Ear models. 3. I had it break the comparison down by noise cancellation, call quality, ecosystem features, and price. 4. I separated must-have features from upgrades I was unlikely to use. 5. I chose the model that met the requirements without paying extra for unnecessary improvements.

Je veux créer N’ose Digital IA, un SaaS international dédié à l’automatisation et conçu de A à Z comme un véritable business SaaS, avec l’IA au cœur de la plateforme. Le projet comprend un dashboard IA de service client ainsi qu’un agent vocal qui répond aux clients et s’appelle « Client ». Step-by-step: 1. Créer le SaaS N’ose Digital IA. 2. Optimiser la plateforme autour de l’automatisation. 3. Concevoir un dashboard IA dédié au service client. 4. Intégrer un agent vocal appelé « Client » pour répondre aux clients. 5. Développer la plateforme comme un business SaaS international, avec l’IA au cœur du projet.

Earlier in my career, I spent a significant amount of time delivering security risk assessments. These engagements typically took 8–12 weeks from the initial client meeting to the final report, with complex projects often stretching to 3–4 months. Today, I can produce reports of comparable quality in 1–2 days. The automation runs end-to-end in about 15 minutes; I spend the remaining time reviewing and validating the output. The workflow starts with a structured questionnaire and uses ChatGPT, Perplexity, Claude, Make.com, Google Drive, and Gamma.ai to research, draft, format, and deliver the report. This compresses the delivery timeline while preserving quality and lets me focus on expert judgment, validation, and client communication instead of manual report production. Step-by-step: 1. I have the client visit a web page and complete a structured questionnaire built on Lovable.dev. 2. The submitted information triggers an automated Make.com workflow. 3. ChatGPT analyzes the client’s responses. 4. Perplexity conducts targeted research tailored to the client’s industry, context, and risk profile. 5. Claude drafts the report and saves it to Google Drive. 6. The draft is passed to Gamma.ai, which generates the final client-ready report. 7. I receive the report by email, perform a thorough review, and send it to the client once I approve it. 8. I use the time saved to focus on expert judgment, output validation, and client communication.